An electrical power storage system and apparatus

Abstract

The application discloses a kind of electric power storage system and equipment, it is related to electric power system technical field.The system includes data acquisition and prediction module, hybrid energy storage device group, intelligent control module, collaborative interaction module and safety protection module.Data acquisition and prediction module obtains historical and predicted environment, power consumption and power supply data;Intelligent control module is based on data calculation storage stability coefficient and power consumption matching index, dynamically controls the charge and discharge of the hybrid energy storage device group consisting of lithium ion battery group, super capacitor, flow battery group;Collaborative interaction module realizes power interaction with new energy equipment and power grid, and safety protection module guarantees reliable operation of system.The application can adapt to new energy volatility, improve power storage efficiency and system stability, and is suitable for new energy consumption, power grid peak shaving and other scenes.

Description

Technical Field

[0001] This invention relates to the field of power systems and their automation technology, and specifically to a power storage system and device. Background Technology

[0002] With the increasing proportion of renewable energy generation such as wind and solar power, their intermittency and volatility significantly impact the stable operation of power systems. Existing power storage systems mostly employ single energy storage devices (such as lithium batteries), which suffer from insufficient response speed and inability to adapt to complex load fluctuations. Furthermore, traditional storage strategies lack collaborative analysis of historical and forecast data, resulting in low renewable energy storage efficiency and poor matching with the grid and load, failing to meet the requirements of a highly reliable power system. Therefore, this invention proposes a data-driven intelligent power storage system and device that integrates multiple types of energy storage devices. Through dynamic optimization of storage strategies, it enhances the renewable energy absorption capacity and system stability. Summary of the Invention

[0003] The purpose of this invention is to provide an energy storage system and device to solve existing problems.

[0004] The technical solution of the present invention to solve the above-mentioned technical problems is as follows:

[0005] An energy storage system, comprising:

[0006] The data acquisition and prediction module is used to acquire environmental data, power consumption data, and power supply data for historical and predicted time periods.

[0007] Hybrid energy storage equipment group, which includes a variety of energy storage devices, is used to store new energy power and grid power;

[0008] The intelligent control module is connected to the data acquisition and prediction module and the hybrid energy storage device group respectively. It is used to calculate the storage stability coefficient and power consumption matching index based on the environmental data, power consumption data and power supply data, and control the charging and discharging process of the hybrid energy storage device group based on the index.

[0009] The collaborative interaction module is connected to the intelligent control module, the new energy power generation equipment and the power grid respectively, and is used to realize the power interaction between the hybrid energy storage equipment group and external equipment.

[0010] In a further embodiment, the data acquisition and prediction module includes:

[0011] The data acquisition unit is used to collect historical environmental data (including wind speed and water temperature), historical power consumption data, and historical power supply data within a historical time period.

[0012] The prediction unit uses time-series prediction algorithms (such as LSTM) to generate predicted environmental data, predicted power consumption data, and predicted power supply data for the prediction period.

[0013] In a further embodiment, the hybrid energy storage device group includes: a lithium-ion battery pack for medium- to long-term energy storage (8-12 hours); a supercapacitor for instantaneous high-power charging and discharging (response time ≤10ms); and a flow battery pack as a backup energy storage device, supporting ≥10,000 charge-discharge cycles.

[0014] In a further embodiment, the lithium-ion battery pack, supercapacitor, and flow battery pack are connected in parallel through a standardized interface, with a single module capacity of 50kWh, supporting multi-module expansion (maximum expansion to 1000kWh), and each device has a built-in temperature and voltage monitoring chip.

[0015] In a further embodiment, the intelligent control module includes: an index calculation unit, used to: calculate the storage stability coefficient based on historical and predicted power supply data fluctuations and the Pearson correlation coefficient of environmental data; and calculate the power consumption matching index based on historical power consumption segment range differences and predicted consumption fluctuations; and a charge / discharge control unit, used to control the charging and discharging logic of the hybrid energy storage device group according to the storage stability coefficient and the power consumption matching index.

[0016] In a further embodiment, the control logic of the charging and discharging control unit includes:

[0017] When the storage stability coefficient is ≥0.6 and the predicted power consumption fluctuation is ≤preset threshold, the lithium-ion battery pack is given priority in storing new energy power.

[0018] When the power generation of new energy sources increases sharply (fluctuation ≥20%), the supercapacitor is triggered to charge rapidly.

[0019] When a peak electricity consumption period is predicted, the flow battery pack is controlled to start discharging 1-2 hours before the peak.

[0020] In a further embodiment, the collaborative interaction module includes:

[0021] The new energy interaction unit connects to wind power and hydropower equipment through a bidirectional inverter and adjusts the new energy access power according to the storage stability coefficient (when the coefficient is <0.3, the access ratio is reduced to below 50%).

[0022] The grid interaction unit is equipped with a smart gateway to replenish and charge the power from the grid during off-peak hours (0-8 am) and release the stored power during peak hours (18-22 pm).

[0023] In a further embodiment, the power grid interaction unit supports peak-valley electricity price arbitrage, automatically switches charging and discharging modes through a built-in electricity price time table, and communicates with the power grid dispatching system in real time to respond to peak-shaving commands.

[0024] Further solutions also include a safety protection module that automatically cuts off the access to new energy sources and switches to grid power supply mode when any device in the hybrid energy storage equipment group is detected to have an abnormal voltage (exceeding the rated value by ±10%) or a storage stability coefficient <0.2.

[0025] In a further embodiment, the data acquisition and prediction module, intelligent control module, and collaborative interaction module are integrated into the same control cabinet, supporting remote communication (4G / 5G / Wi-Fi), and the system status can be monitored and control parameters adjusted in real time through the user terminal.

[0026] A power storage device includes: at least one processor, at least one memory, a communication interface, and a bus; wherein the processor, memory, and communication interface communicate with each other via the bus; the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to implement the aforementioned power storage system.

[0027] The present invention has the following beneficial effects:

[0028] This invention employs a hybrid energy storage device group, which takes into account both long-term energy storage and instantaneous response requirements, and is adaptable to fluctuations in new energy power generation; based on the collaborative analysis of historical and predictive data, it dynamically optimizes the storage strategy to improve the stability of the power system; it supports coordinated operation with new energy equipment and the power grid to improve energy utilization efficiency and economy; and it integrates a safety protection mechanism to ensure reliable system operation. Attached Figure Description

[0029] Figure 1 This is a diagram of the overall system architecture of the present invention.

[0030] Figure 2 This is a schematic diagram of the data acquisition and prediction unit in this invention.

[0031] Figure 3 This is a logic block diagram showing the operating state of the hybrid energy storage device in this invention. Detailed Implementation

[0032] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0033] I. System Overall Architecture

[0034] The overall architecture of power storage systems and equipment is as follows: Figure 1As shown, it specifically includes a data acquisition and prediction module, a hybrid energy storage device group, an intelligent control module, a collaborative interaction module, and a security protection module. All modules communicate via industrial Ethernet, with a data transmission rate ≥100Mbps and a response latency ≤50ms.

[0035] II. Data Acquisition and Prediction Module and Data Acquisition Unit

[0036] Deploy devices such as temperature and humidity sensors, anemometers, and power sensors to collect the following data: Environmental data: wind speed (m / s), light intensity (W / m²), ambient temperature (°C), sampling frequency 1 time / minute; Power consumption data: real-time load on the user side (kW), sampling frequency 1 time / 10 seconds; Power supply data: renewable energy generation power (wind power / photovoltaic, kW), grid power supply power (kW), sampling frequency 1 time / 10 seconds. The historical time period is set to the previous 24 hours, and the data is stored on an industrial-grade solid-state drive with a capacity of ≥1TB.

[0037] The prediction unit generates data for the next 24 hours based on the LSTM (Long Short-Term Memory) algorithm. The specific process is as follows: Input: Historical environmental data, power consumption data, and power supply data (time series) for the past 24 hours; Model training: Adam optimizer is used with a learning rate of 0.001, 500 iterations, and the loss function is mean squared error (MSE); Output: Predicted environmental data (such as wind speed prediction value v_pred(t)), predicted power consumption data P_cons_pred(t), and predicted power supply data P_sup_pred(t), with a prediction step size of 15 minutes.

[0038] III. Hybrid Energy Storage Equipment Group: Lithium-ion Battery Pack Technical Parameters: Capacity 50kWh / module, Voltage Rating 380V, Cycle Life ≥3000 cycles, Charge / Discharge Efficiency ≥90%; Function: To provide medium- to long-term energy storage (8-12 hours), suitable for stable load storage needs. Supercapacitor Technical Parameters: Capacity 500F, Voltage Rating 400V, Response Time ≤10ms, Power Density ≥1000W / kg; Function: To handle instantaneous high-power charge / discharge (such as sudden increases in new energy power generation / load fluctuations). Flow Battery Pack Technical Parameters: Capacity 100kWh / module, Cycle Life ≥10000 cycles, Charge / Discharge Efficiency ≥75%; Function: As backup energy storage, suitable for storing new energy power with large fluctuations.

[0039] Connection method: The three devices are connected in parallel through a standardized DC / DC interface, supporting multi-module expansion (maximum expansion to 1000kWh). Each device has built-in voltage, current and temperature sensors (measurement accuracy ±0.5%) to provide real-time feedback on operating status.

[0040] IV. Intelligent Control Module Index Calculation Unit

[0041] (1) Storage stability coefficient S

[0042] The calculation steps are as follows: This is used to quantify the ability of an energy storage system to withstand fluctuations in new energy sources.

[0043] Step 1: Calculate the historical power supply fluctuation index σ_hist:

[0044] ;

[0045] in, Historical power supply data, is the historical average power supply, and n is the historical data sample size.

[0046] Step 2: Calculate the predicted power supply fluctuation index σ_pred:

[0047] ;

[0048] in, To predict power supply data, The value is the predicted average power supply, and m is the predicted data sample size.

[0049] Step 3: Calculate the correlation index r (Pearson correlation coefficient) of environmental data:

[0050] ;

[0051] in, , These represent historical and predicted environmental data, respectively, with D representing the environmental data dimension (e.g., wind speed, temperature, D≥1). This is the average value of historical environmental data. To predict the average value of environmental data.

[0052] Step 4: Calculate the storage stability coefficient S:

[0053] (Normalization process, S∈[0,1], the larger the value, the higher the stability).

[0054] (2) Electricity consumption matching index C

[0055] The calculation steps are as follows: This is used to quantify the match between energy storage output and load demand.

[0056] Step 1: Calculate the historical electricity consumption mutation index M_hist:

[0057] The historical time period (24 hours) is divided into 12 segments (each segment lasting 2 hours), and the power consumption range of each segment is calculated:

[0058] ;

[0059] Difference in range between adjacent sub-segments: ;

[0060] Historical mutation indicators: ;

[0061] Step 2: Calculate the predicted electricity consumption fluctuation index :

[0062]

[0063] Step 3: Calculate the power consumption matching index C:

[0064] (Normalization process, C∈[0,1], the larger the value, the higher the matching degree);

[0065] : Represents the electricity consumption value for the i-th historical period, where It is used to obtain electricity consumption data for different historical periods, providing a basis for subsequent calculation of relevant indicators of historical electricity consumption.

[0066] : Represents the predicted power consumption value for the j-th forecast period. It is used to describe the power consumption situation in each forecast period and is a key data source for calculating the predicted power consumption fluctuation index.

[0067] This is the average value of predicted electricity consumption, calculated by analyzing electricity consumption forecasts over multiple forecast periods. ( The average value is used to measure the central tendency of predicted electricity consumption, combined with... It can calculate and predict the degree of fluctuation in electricity consumption;

[0068] The charge / discharge control unit executes the following control logic based on indicators S and C:

[0069] When S≥0.6 and C≥0.7: Prioritize charging of lithium-ion battery packs, with charging current ≤0.5C (C is the battery capacity ratio); When the power generation of new energy sources increases sharply (ΔP_sup≥20%×rated power): Trigger fast charging of supercapacitors, with a response time ≤10ms; 1-2 hours before the predicted peak electricity consumption (P_cons_pred(t)≥1.2×average load): Control the flow battery pack to discharge at a rate of 0.3C to supplement the grid power supply; During charging and discharging, adjust the power distribution of each device in real time to ensure that the deviation between the total charging and discharging power and the predicted power supply / consumption difference is ≤5%.

[0070] V. Collaborative Interaction Module: New Energy Interaction Unit

[0071] The system connects to wind / photovoltaic equipment via a bidirectional inverter (conversion efficiency ≥96%), and adjusts the access power according to the storage stability coefficient S: when S≥0.5, the new energy access power is ≤80% of the rated capacity of the hybrid energy storage equipment group; when S<0.3, the new energy access power is reduced to below 50% of the rated capacity to avoid overloading the storage system.

[0072] The grid interaction unit is equipped with an intelligent gateway to support communication with the grid dispatch system: During grid off-peak hours (0:00-8:00): it replenishes the power from the grid, with the charging power ≤ 60% of the rated power of the energy storage system; During grid peak hours (18:00-22:00): it releases the stored power, with the discharge power ≤ 70% of the rated power of the energy storage system; In response to grid peak shaving commands, the charging and discharging power can be adjusted by ±20% within 15 minutes.

[0073] VI. Anomaly Monitoring of Security Protection Module

[0074] Real-time monitoring of voltage (U), current (I), and temperature (T) of hybrid energy storage equipment: Voltage abnormality: U < 0.9 × rated voltage or U > 1.1 × rated voltage; Temperature abnormality: T > 50℃ (lithium-ion battery), T > 60℃ (flow battery).

[0075] When an abnormality is detected or S < 0.2, the protection action is as follows: immediately disconnect the new energy source and switch to grid power supply mode; trigger an audible and visual alarm and send a fault code to the operation and maintenance terminal via the 4G module.

[0076] VII. System Integration and Operation & Maintenance

[0077] The data acquisition and prediction module, intelligent control module, and collaborative interaction module are integrated into the control cabinet (dimensions: 800mm×600mm×300mm), with an IP54 protection rating; it supports remote communication (4G / 5G / Wi-Fi), and users can monitor the system status (such as SOC, charging and discharging power) in real time through a web terminal or mobile APP, and can remotely adjust control parameters (such as charging and discharging thresholds); the system's annual mean time between failures (MTBF) is ≥8000 hours.

[0078] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An electrical power storage system, characterized by, The application relates to a hybrid energy storage system and a control method thereof. The hybrid energy storage system comprises: a data acquisition and prediction module for acquiring environmental data, power consumption data and power supply data in a historical period and a prediction period; a hybrid energy storage device group comprising multiple energy storage devices for storing new energy power and grid power; an intelligent control module connected with the data acquisition and prediction module and the hybrid energy storage device group, for calculating a storage stability coefficient and a power consumption matching index according to the environmental data, the power consumption data and the power supply data, and controlling the charging and discharging process of the hybrid energy storage device group based on the index; a cooperative interaction module connected with the intelligent control module, new energy power generation equipment and a power grid, for realizing power interaction between the hybrid energy storage device group and external equipment. The intelligent control module comprises:

2. The power storage system of claim 1, wherein, an index calculation unit for calculating the storage stability coefficient based on the Pearson correlation coefficient of the historical and predicted power supply data fluctuation and the environmental data; a power consumption matching index is calculated based on the historical power consumption sub-range range difference and the predicted consumption fluctuation; and a charging and discharging control unit for controlling the charging and discharging logic of the hybrid energy storage device group according to the storage stability coefficient and the power consumption matching index.

3. The power storage system of claim 1, wherein, The data acquisition and prediction module comprises:

4. The power storage system of claim 3, wherein, a data acquisition unit for acquiring historical environmental data, historical power consumption data and historical power supply data in a historical period, wherein the historical environmental data comprises wind speed and water temperature; 5. The power storage system of claim 1, wherein, a prediction unit for generating predicted environmental data, predicted power consumption data and predicted power supply data in a prediction period by using a time series prediction algorithm. The hybrid energy storage device group comprises: a lithium ion battery group for medium and long time energy storage; a super capacitor for instantaneous high-power charging and discharging; 6. The power storage system of claim 1, wherein, a flow battery group as a backup energy storage device supporting cyclic charging and discharging of more than 10,000 times. The lithium ion battery group, the super capacitor and the flow battery group are connected in parallel through a standardized interface, a single module capacity is 50 kWh, multiple module expansion is supported, and temperature and voltage monitoring chips are built in each device. The control logic of the charging and discharging control unit comprises:

7. The power storage system of claim 6, wherein, when the storage stability coefficient is greater than or equal to 0.6 and the predicted power consumption fluctuation is less than or equal to a preset threshold, the lithium ion battery group is preferentially controlled to store new energy power; when new energy power generation power suddenly increases, the fluctuation is greater than or equal to 20%, and the super capacitor is triggered to charge rapidly; when there is a power consumption peak in the prediction period, the flow battery group is controlled to start discharging 1-2 hours before the peak. The cooperative interaction module comprises: a new energy interaction unit connected with wind power and water power equipment through a bidirectional inverter, and the new energy access power is adjusted according to the storage stability coefficient, and when the coefficient is less than 0.3, the access ratio is reduced to below 50%; a power grid interaction unit configured with an intelligent gateway, which supplements charging from the power grid in a power grid low valley period and releases stored power in a power peak period. The power grid interaction unit supports peak-valley electricity price arbitrage, automatically switches the charging and discharging mode through a built-in electricity price period table, and communicates with a power grid dispatching system in real time to respond to peak shaving instructions.

8. The power storage system of claim 1, wherein, Also include security protection module, when detecting any device voltage of hybrid energy storage device group is abnormal, exceeds rated value ± 10%, or storage stability coefficient < 0.2, automatically cut off new energy access, and switch to grid power supply mode.

9. An electrical power storage device, characterized by, Comprise: At least one processor, at least one memory, a communication interface and a bus; wherein the processor, memory, communication interface complete the communication among each other through the bus; The memory stores program instructions executable by the processor, and the processor invokes the program instructions to implement the system according to any one of claims 1 to 8.

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